Waste is a massive problem: the world generates 1.3 billion tons of municipal solid waste each year, according to the World Bank. By 2025, that figure is expected to hit 2.2 billion tons.
What’s worse, recycling efforts aren’t helping as much as people assume. For instance, a scant 14 percent of plastic is recycled globally. In the U.S., roughly a third of all waste is recycled, according to the Environmental Protection Agency (EPA), a figure that has been static for a decade.
Conventional sorters have improved the recycling process, but it’s not enough, said Brooks. Sorters identify material composition through infrared cameras using optical sensors, then mechanical sorters, such as blowers, arrange the garbage. But at many MRFs, recycling workers are needed to segregate it further.
After recyclables are sorted, processed and baled, MRFs may sell the materials to brokers or manufacturing plants, which have specific requirements around what kind of materials they’ll take.
For example, a plastic clamshell salad package is made from the same material as a plastic water bottle. To an infrared camera, that clamshell and water bottle look the same, because they’re made from the equivalent type of plastic. However, the MRF often can’t sell both materials to the same broker because they might refuse material with food contamination.
But computer vision systems, the brains that power new robotic sorters, can easily tell the difference between similar materials, said Matanya Horowitz, founder and CEO of AMP Robotics.
Horowitz was a graduate student at CalTech when he realized recycling sorting systems are ripe for the deep learning techniques he was studying.